Feature selection methods in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SelectKBest feature selector from scikit-learn.

ML Python

from sklearn.feature_selection import [1]

Drag options to blanks, or click blank then click option'

AStandardScaler

BPCA

Ctrain_test_split

DSelectKBest

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing PCA which is for dimensionality reduction, not feature selection.

Using train_test_split which is for splitting data, not feature selection.

The SelectKBest class is used for feature selection by selecting the top k features based on a scoring function.

2fill in blank

medium

Complete the code to select the top 5 features using SelectKBest with the chi2 scoring function.

ML Python

selector = SelectKBest(score_func=[1], k=5)

Drag options to blanks, or click blank then click option'

Amutual_info_classif

Bf_classif

Cchi2

Dr2_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using f_classif which is for ANOVA F-value, not chi-squared.

Using r2_score which is a regression metric, not a scoring function for feature selection.

The chi2 function is a common scoring function used with SelectKBest for classification tasks with non-negative features.

3fill in blank

hard

Fix the error in the code to correctly fit the feature selector to the data X and labels y.

ML Python

selector = SelectKBest(score_func=chi2, k=3)
selector.[1](X, y)

Drag options to blanks, or click blank then click option'

Afit

Btransform

Cpredict

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting causes an error.

Using predict is not valid for feature selectors.

The fit method trains the selector to find the best features based on X and y.

4fill in blank

hard

Fill both blanks to create a dictionary of feature scores and select features with scores greater than 10.

ML Python

scores = {feature: score for feature, score in zip(feature_names, selector.scores_)}
selected_features = [feature for feature, score in scores.items() if score [1] [2]]

Drag options to blanks, or click blank then click option'

A>

B10

C<

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' selects low scoring features.

Using 5 instead of 10 changes the threshold.

This code selects features whose scores are greater than 10.

5fill in blank

hard

Fill all three blanks to create a new dataset X_new with selected features and print its shape.

ML Python

X_new = selector.[1](X)
print('Selected features shape:', X_new.[2])
num_features = X_new.[3]

Drag options to blanks, or click blank then click option'

Atransform

Bshape

Cshape[1]

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of transform to get selected features.

Using shape[0] instead of shape[1] to get feature count.

The transform method applies feature selection to X. shape gives the dimensions of the array, and shape[1] gives the number of columns (features).

Practice

1. Which of the following best describes the purpose of feature selection in machine learning?

easy

A. To choose the most important features to improve model performance

B. To increase the number of features in the dataset

C. To randomly remove features from the dataset

D. To convert features into labels for training

Solution

1. Step 1: Understand feature selection goal

   Feature selection aims to pick the most useful features that help the model learn better.

2. Step 2: Evaluate options

   Only To choose the most important features to improve model performance correctly states that feature selection chooses important features to improve model performance.

3. Final Answer:

   To choose the most important features to improve model performance -> Option A

4. Quick Check:

   Feature selection = pick important features [OK]

Hint: Feature selection picks useful features, not random or all [OK]

Common Mistakes:

• Thinking feature selection adds features
• Confusing feature selection with feature engineering
• Believing feature selection changes labels

2. Which Python library provides the SelectKBest feature selection method?

easy

A. pandas

B. scikit-learn

C. numpy

D. matplotlib

Solution

1. Step 1: Recall common ML libraries

   Scikit-learn is the main library for machine learning tools including feature selection.

2. Step 2: Match method to library

   SelectKBest is part of scikit-learn's feature_selection module, not pandas, numpy, or matplotlib.

3. Final Answer:

   scikit-learn -> Option B

4. Quick Check:

   SelectKBest = scikit-learn [OK]

Hint: SelectKBest is from scikit-learn, not data or plotting libs [OK]

Common Mistakes:

• Choosing pandas because it handles data
• Confusing numpy with ML feature tools
• Selecting matplotlib which is for plotting

3. What will be the output shape of features after applying VarianceThreshold(threshold=0.1) on a dataset with shape (100, 5) where only 3 features have variance above 0.1?

medium

A. (5, 100)

B. (100, 5)

C. (3, 100)

D. (100, 3)

Solution

1. Step 1: Understand VarianceThreshold effect

   VarianceThreshold removes features with variance below the threshold, keeping only those above it.

2. Step 2: Apply to given data

   Since 3 features have variance above 0.1, only those 3 remain. The number of samples (100) stays the same.

3. Final Answer:

   (100, 3) -> Option D

4. Quick Check:

   VarianceThreshold keeps features with variance > threshold [OK]

Hint: Output shape keeps rows, columns = features passing threshold [OK]

Common Mistakes:

• Confusing rows and columns in shape
• Assuming all features remain
• Thinking variance threshold changes sample count

4. Consider this code snippet:

from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
rfe = RFE(model, n_features_to_select=2)
rfe.fit(X, y)
selected = rfe.transform(X)
print(selected.shape)

If X has shape (50, 4), but the output shape is (50, 4), what is the likely error?

medium

A. RFE does not reduce features automatically

B. n_features_to_select is greater than number of features

C. RFE was not fitted before transform

D. LogisticRegression model is incompatible with RFE

Solution

1. Step 1: Understand RFE usage

   RFE must be fitted before calling transform to reduce features.

2. Step 2: Check given code and output

   If output shape is unchanged, likely transform was called before fitting or fitting failed.

3. Step 3: Identify cause

   Since code shows fitting before transform, but output shape unchanged, the most common cause is that transform was called on unfitted RFE or fit did not complete properly.

4. Final Answer:

   RFE was not fitted before transform -> Option C

5. Quick Check:

   Fit RFE before transform to reduce features [OK]

Hint: Ensure RFE is fitted before transform [OK]

Common Mistakes:

• Assuming transform always reduces features without fitting
• Ignoring the need to fit RFE
• Thinking model type causes shape issue

5. You have a dataset with 10 features, but 4 are highly correlated and 2 have very low variance. Which feature selection approach best improves model simplicity and speed?

hard

A. Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features

B. Use RFE with all features and keep all 10

C. Use SelectKBest to pick top 6 features by univariate scores

D. Randomly drop 4 features to reduce dimensionality

Solution

1. Step 1: Identify problem features

   Low variance features add little info; correlated features add redundancy.

2. Step 2: Choose method to remove both

   VarianceThreshold removes low variance features; correlation filter removes redundant correlated features.

3. Step 3: Evaluate options

   Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features combines both methods to improve simplicity and speed effectively.

4. Final Answer:

   Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features -> Option A

5. Quick Check:

   Remove low variance + correlated features = simpler model [OK]

Hint: Combine variance and correlation filters for best feature reduction [OK]

Common Mistakes:

• Using only one method ignoring other feature issues
• Randomly dropping features without reason
• Keeping all features with RFE without reduction